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Epigenetic clocks, sex markers and age-class diagnostics in three harvested large mammals.

التفاصيل البيبلوغرافية
العنوان: Epigenetic clocks, sex markers and age-class diagnostics in three harvested large mammals.
المؤلفون: Czajka N; Department of Environmental Life Sciences Graduate Program, Trent University, Peterborough, Ontario, Canada., Northrup JM; Department of Environmental Life Sciences Graduate Program, Trent University, Peterborough, Ontario, Canada.; Wildlife Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, Peterborough, Ontario, Canada., Jones MJ; Department of Biochemistry and Medical Genetics, Children's Hospital Research Institute of Manitoba, University of Manitoba, Winnipeg, Manitoba, Canada., Shafer ABA; Department of Environmental Life Sciences Graduate Program, Trent University, Peterborough, Ontario, Canada.; Department of Forensic Science, Trent University, Peterborough, Ontario, Canada.
المصدر: Molecular ecology resources [Mol Ecol Resour] 2024 Jul; Vol. 24 (5), pp. e13956. Date of Electronic Publication: 2024 Mar 30.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Blackwell Country of Publication: England NLM ID: 101465604 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1755-0998 (Electronic) Linking ISSN: 1755098X NLM ISO Abbreviation: Mol Ecol Resour Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Oxford, England : Blackwell
مواضيع طبية MeSH: DNA Methylation*/genetics , Deer*/genetics , Deer*/classification , Epigenesis, Genetic*/genetics, Animals ; Ursidae/genetics ; Ursidae/classification ; Female ; Male ; CpG Islands/genetics ; Aging/genetics ; North America ; Goats/genetics
مستخلص: The development of epigenetic clocks, or the DNA methylation-based inference of age, is an emerging tool for ageing in free ranging populations. In this study, we developed epigenetic clocks for three species of large mammals that are the focus of extensive management throughout their range in North America: white-tailed deer, black bear and mountain goat. We quantified differential DNA methylation patterns at over 30,000 cytosine-guanine sites (CpGs) from tissue samples of all three species (black bear n = 49; white-tailed deer n = 47; mountain goat n = 45). We used a penalized regression model (elastic net) to build explanatory (black bear r = .95; white-tailed deer r = .99; mountain goat r = .97) and robust (black bear Median Absolute Error or MAE = 1.33; white-tailed deer MAE = 0.29; mountain goat MAE = 0.61) models of age or clocks. We also characterized individual CpG sites within each species that demonstrated clear differences in methylation levels between age classes and sex, which can be used to develop a suite of accessible diagnostic markers. This tool has the potential to contribute to wildlife monitoring by providing easily obtainable representations of age structure in managed populations.
(© 2024 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.)
References: Arneson, A., Haghani, A., Thompson, M. J., Pellegrini, M., Kwon, S. B., Vu, H., Maciejewski, E., Yao, M., Li, C. Z., Lu, A. T., Morselli, M., Rubbi, L., Barnes, B., Hansen, K. D., Zhou, W., Breeze, C. E., Ernst, J., & Horvath, S. (2022). A mammalian methylation array for profiling methylation levels at conserved sequences. Nature Communications, 13(1), 783. https://doi.org/10.1038/s41467‐022‐28355‐z.
Beier, L. R., Lewis, S. B., Flynn, R. W., Pendleton, G., & Schumacher, T. V. (2005). From the field: A single‐catch snare to collect brown bear hair for genetic mark‐recapture studies. Wildlife Society Bulletin, 33(2), 766–773. https://doi.org/10.2193/0091‐7648(2005)33[766:FTFASS]2.0.CO;2.
Bendixen, K. K., Mindegaard, M., Epistolio, S., Dazio, G., Marchi, F., Spina, P., Arnspang, E. C., Soerensen, M., Christensen, U. B., Frattini, M., & Petersen, R. K. (2023). A qPCR technology for direct quantification of methylation in untreated DNA. Nature Communications, 14(1), 5153. https://doi.org/10.1038/s41467‐023‐40873‐y.
Bobak, C. A., Abhimanyu, Natarajan, H., Gandhi, T., Grimm, S. L., Nishiguchi, T., Koster, K., Longlax, S. C., Dlamini, Q., Kahari, J., Mtetwa, G., Cirillo, J. D., O'Malley, J., Hill, J. E., Coarfa, C., & DiNardo, A. R. (2022). Increased DNA methylation, cellular senescence and premature epigenetic aging in guinea pigs and humans with tuberculosis. Aging, 14(5), 2174–2193. https://doi.org/10.18632/aging.203936.
Brisotto, G., di Gennaro, A., Damiano, V., Armellin, M., Perin, T., Maestro, R., & Santarosa, M. (2015). An improved sequencing‐based strategy to estimate locus‐specific DNA methylation. BMC Cancer, 15(1), 639. https://doi.org/10.1186/s12885‐015‐1646‐6.
Caulton, A., Dodds, K. G., McRae, K. M., Couldrey, C., Horvath, S., & Clarke, S. M. (2021). Development of epigenetic clocks for key ruminant species. Genes, 13(1), 96. https://doi.org/10.3390/genes13010096.
Chinnadurai, S. K., Strahl‐Heldreth, D., Fiorello, C. V., & Harms, C. A. (2016). Best‐practice guidelines for field‐based surgery and anesthesia of free‐ranging wildlife. I. Anesthesia and analgesia. Journal of Wildlife Diseases, 52(2s), S14–S27. https://doi.org/10.7589/52.2S.S14.
Davis, K. L., Silverman, E. D., Sussman, A. L., Wilson, R. R., & Zipkin, E. F. (2022). Errors in aerial survey count data: Identifying pitfalls and solutions. Ecology and Evolution, 12(3), e8733. https://doi.org/10.1002/ece3.8733.
Dhar, G. A., Saha, S., Mitra, P., & Nag Chaudhuri, R. (2021). DNA methylation and regulation of gene expression: Guardian of our health. The Nucleus, 64(3), 259–270. https://doi.org/10.1007/s13237‐021‐00367‐y.
Fleckhaus, J., & Schneider, P. M. (2020). Novel multiplex strategy for DNA methylation‐based age prediction from small amounts of DNA via pyrosequencing. Forensic Science International: Genetics, 44, 102189. https://doi.org/10.1016/j.fsigen.2019.102189.
Foley, A. M., Lewis, J. S., Cortez, O., Hellickson, M. W., Hewitt, D. G., DeYoung, R. W., DeYoung, C. A., Schnupp, M. J., Foley, A. M., Lewis, J. S., Cortez, O., Hellickson, M. W., Hewitt, D. G., DeYoung, R. W., DeYoung, C. A., & Schnupp, M. J. (2021). Accuracies and biases of ageing white‐tailed deer in semiarid environments. Wildlife Research, 49(3), 237–249. https://doi.org/10.1071/WR21050.
Fortin, J.‐P., Triche, T. J., Jr., & Hansen, K. D. (2017). Preprocessing, normalization and integration of the Illumina HumanMethylationEPIC array with minfi. Bioinformatics, 33(4), 558–560. https://doi.org/10.1093/bioinformatics/btw691.
Friedman, J. H., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33, 1–22. https://doi.org/10.18637/jss.v033.i01.
Gaidatzis, D., Lerch, A., Hahne, F., & Stadler, M. B. (2015). QuasR: Quantification and annotation of short reads in R. Bioinformatics, 31(7), 1130–1132. https://doi.org/10.1093/bioinformatics/btu781.
Gallego‐Bartolomé, J. (2020). DNA methylation in plants: Mechanisms and tools for targeted manipulation. New Phytologist, 227(1), 38–44. https://doi.org/10.1111/nph.16529.
Gasaway, W. C., Harkness, D. B., & Rausch, R. A. (1978). Accuracy of moose age determinations from incisor cementum layers. The Journal of Wildlife Management, 42(3), 558–563. https://doi.org/10.2307/3800818.
Han, Y., Eipel, M., Franzen, J., Sakk, V., Dethmers‐Ausema, B., Yndriago, L., Izeta, A., de Haan, G., Geiger, H., & Wagner, W. (2018). Epigenetic age‐predictor for mice based on three CpG sites. eLife, 7, e37462. https://doi.org/10.7554/eLife.37462.
Hao, T., Guo, J., Liu, J., Wang, J., Liu, Z., Cheng, X., Li, J., Ren, J., Li, Z., Yan, J., & Zhang, G. (2021). Predicting human age by detecting DNA methylation status in hair. Electrophoresis, 42(11), 1255–1261. https://doi.org/10.1002/elps.202000349.
Harris, R. B., & Metzgar, L. H. (1987). Harvest age structures as indicators of decline in small populations of grizzly bears. Bears: Their Biology and Management, 7, 109–116. https://doi.org/10.2307/3872616.
Harshyne, W. A., Diefenbach, D. R., Alt, G. L., & Matson, G. M. (1998). Analysis of error from cementum‐annuli age estimates of known‐age Pennsylvania black bears. The Journal of Wildlife Management, 62(4), 1281–1291. https://doi.org/10.2307/3801992.
Hayes, B. J., Nguyen, L. T., Forutan, M., Engle, B. N., Lamb, H. J., Copley, J. P., Randhawa, I. A. S., & Ross, E. M. (2021). An epigenetic aging clock for cattle using portable sequencing technology. Frontiers in Genetics, 12, 760450. https://doi.org/10.3389/fgene.2021.760450.
Hecht, L. (2021). The importance of considering age when quantifying wild animals' welfare. Biological Reviews, 96(6), 2602–2616. https://doi.org/10.1111/brv.12769.
Horvath, S., & Raj, K. (2018). DNA methylation‐based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics, 19(6), 371–384. https://doi.org/10.1038/s41576‐018‐0004‐3.
Hristienko, H., & McDonald, J. E. (2007). Going into the 21st century: A perspective on trends and controversies in the management of the American Black Bear. Ursus, 18(1), 72–88.
Jones, M. J., Goodman, S. J., & Kobor, M. S. (2015). DNA methylation and healthy human aging. Aging Cell, 14(6), 924–932. https://doi.org/10.1111/acel.12349.
Jung, M., & Pfeifer, G. P. (2015). Aging and DNA methylation. BMC Biology, 13(1), 7. https://doi.org/10.1186/s12915‐015‐0118‐4.
Kendall, K. C., Stetz, J. B., Roon, D. A., Waits, L. P., Boulanger, J. B., & Paetkau, D. (2008). Grizzly bear density in glacier National Park, Montana. The Journal of Wildlife Management, 72(8), 1693–1705. https://doi.org/10.2193/2008‐007.
Larison, B., Pinho, G. M., Haghani, A., Zoller, J. A., Li, C. Z., Finno, C. J., Farrell, C., Kaelin, C. B., Barsh, G. S., Wooding, B., Robeck, T. R., Maddox, D., Pellegrini, M., & Horvath, S. (2021). Epigenetic models developed for plains zebras predict age in domestic horses and endangered equids. Communications Biology, 4, 1412. https://doi.org/10.1038/s42003‐021‐02935‐z.
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012). The sva package for removing batch effects and other unwanted variation in high‐throughput experiments. Bioinformatics, 28(6), 882–883. https://doi.org/10.1093/bioinformatics/bts034.
Liu, C., Xu, L., Li, W., Jie, M., Xue, W., & Yu, W. (2021). Multiple biomarker‐combined screening for colorectal cancer based on bisulfate conversion‐free detection of fecal DNA methylation. BioMed Research International, 2021, e1479748. https://doi.org/10.1155/2021/1479748.
Lu, A. T., Fei, Z., Haghani, A., Robeck, T. R., Zoller, J. A., Li, C. Z., Lowe, R., Yan, Q., Zhang, J., Vu, H., Ablaeva, J., Acosta‐Rodriguez, V. A., Adams, D. M., Almunia, J., Aloysius, A., Ardehali, R., Arneson, A., Baker, C. S., Banks, G., … Horvath, S. (2023). Universal DNA methylation age across mammalian tissues. Nature Aging, 3, 1144–1166. https://doi.org/10.1038/s43587‐023‐00462‐6.
Lyko, F. (2018). The DNA methyltransferase family: A versatile toolkit for epigenetic regulation. Nature Reviews Genetics, 19(2), 81–92. https://doi.org/10.1038/nrg.2017.80.
McShea, W. J. (2012). Ecology and management of white‐tailed deer in a changing world. Annals of the New York Academy of Sciences, 1249(1), 45–56. https://doi.org/10.1111/j.1749‐6632.2011.06376.x.
Milner, J. M., Nilsen, E. B., & Andreassen, H. P. (2007). Demographic side effects of selective hunting in ungulates and carnivores. Conservation Biology, 21(1), 36–47. https://doi.org/10.1111/j.1523‐1739.2006.00591.x.
Moore, L. D., Le, T., & Fan, G. (2013). DNA methylation and its basic function. Neuropsychopharmacology, 38(1), 23–38. https://doi.org/10.1038/npp.2012.112.
Pacht, E., Murdoch, B., & McKay, S. (2021). Examining the extent of environmental contributions toward DNA methylation and phenotypic variation. Animal Frontiers, 11(6), 83–89. https://doi.org/10.1093/af/vfab056.
Parlee, B., Ahkimnachie, K., Cunningham, H., Jordan, M., & Goddard, E. (2021). “It's important to know about this”—Risk communication and the impacts of chronic wasting disease on indigenous food systems in Western Canada. Environmental Science & Policy, 123, 190–201. https://doi.org/10.1016/j.envsci.2021.05.012.
Peters, K. J., Gerber, L., Scheu, L., Cicciarella, R., Zoller, J. A., Fei, Z., Horvath, S., Allen, S. J., King, S. L., Connor, R. C., Rollins, L. A., & Krützen, M. (2023). An epigenetic DNA methylation clock for age estimates in indo‐Pacific bottlenose dolphins (Tursiops aduncus). Evolutionary Applications, 16(1), 126–133. https://doi.org/10.1111/eva.13516.
Polanowski, A. M., Robbins, J., Chandler, D., & Jarman, S. N. (2014). Epigenetic estimation of age in humpback whales. Molecular Ecology Resources, 14(5), 976–987. https://doi.org/10.1111/1755‐0998.12247.
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). Limma powers differential expression analyses for RNA‐sequencing and microarray studies. Nucleic Acids Research, 43(7), e47. https://doi.org/10.1093/nar/gkv007.
Robeck, T. R., Fei, Z., Lu, A. T., Haghani, A., Jourdain, E., Zoller, J. A., Li, C. Z., Steinman, K. J., DiRocco, S., Schmitt, T., Osborn, S., Van Bonn, B., Katsumata, E., Mergl, J., Almunia, J., Rodriguez, M., Haulena, M., Dold, C., & Horvath, S. (2021). Multi‐species and multi‐tissue methylation clocks for age estimation in toothed whales and dolphins. Communications Biology, 4(1), 642. https://doi.org/10.1038/s42003‐021‐02179‐x.
Robeck, T. R., Haghani, A., Fei, Z., Lindemann, D. M., Russell, J., Herrick, K. E. S., Montano, G., Steinman, K. J., Katsumata, E., Zoller, J. A., & Horvath, S. (2023). Multi‐tissue DNA methylation aging clocks for sea lions, walruses and seals. Communications Biology, 6(1), 359. https://doi.org/10.1038/s42003‐023‐04734‐0.
Sato, A., Hiura, H., Okae, H., Miyauchi, N., Abe, Y., Utsunomiya, T., Yaegashi, N., & Arima, T. (2011). Assessing loss of imprint methylation in sperm from subfertile men using novel methylation polymerase chain reaction Luminex analysis. Fertility and Sterility, 95(1), 129–134, 134.e1‐4. https://doi.org/10.1016/j.fertnstert.2010.06.076.
Schuster, R. C., Wein, E. E., Dickson, C., & Chan, H. M. (2011). Importance of traditional foods for the food security of two first nations communities in the Yukon, Canada. International Journal of Circumpolar Health, 70(3), 286–300. https://doi.org/10.3402/ijch.v70i3.17833.
Sinke, L., Cats, D., & Heijmans, B. T. (2021). Omixer: Multivariate and reproducible sample randomization to proactively counter batch effects in omics studies. Bioinformatics, 37(18), 3051–3052. https://doi.org/10.1093/bioinformatics/btab159.
Smith, B. L. (1988). Criteria for determining age and sex of American Mountain goats in the field. Journal of Mammalogy, 69(2), 395–402. https://doi.org/10.2307/1381400.
Solberg, E. J., Saether, B.‐E., Strand, O., & Loison, A. (1999). Dynamics of a harvested moose population in a variable environment. Journal of Animal Ecology, 68(1), 186–204. https://doi.org/10.1046/j.1365‐2656.1999.00275.x.
Stevens, V., & Houston, D. B. (1989). Reliability of age determination of mountain goats. Wildlife Society Bulletin (1973–2006), 17(1), 72–74.
Storm, D. J., Samuel, M. D., Rolley, R. E., Beissel, T., Richards, B. J., & Van Deelen, T. R. (2014). Estimating ages of white‐tailed deer: Age and sex patterns of error using tooth wear‐and‐replacement and consistency of cementum annuli. Wildlife Society Bulletin, 38(4), 849–856. https://doi.org/10.1002/wsb.457.
Tangili, M., Slettenhaar, A. J., Sudyka, J., Dugdale, H. L., Pen, I., Palsbøll, P. J., & Verhulst, S. (2023). DNA methylation markers of age(ing) in non‐model animals. Molecular Ecology, 32, 4725–4741. https://doi.org/10.1111/mec.17065.
Teschendorff, A. E., West, J., & Beck, S. (2013). Age‐associated epigenetic drift: Implications, and a case of epigenetic thrift? Human Molecular Genetics, 22(R1), R7–R15. https://doi.org/10.1093/hmg/ddt375.
Tryland, M., Beckmen, K. B., Burek‐Huntington, K. A., Breines, E. M., & Klein, J. (2018). Orf virus infection in Alaskan mountain goats, Dall's sheep, muskoxen, caribou and Sitka black‐tailed deer. Acta Veterinaria Scandinavica, 60(1), 12. https://doi.org/10.1186/s13028‐018‐0366‐8.
Udevitz, M. S., & Ballachey, B. E. (1998). Estimating survival rates with age‐structure data. The Journal of Wildlife Management, 62(2), 779–792. https://doi.org/10.2307/3802355.
Unnikrishnan, A., Freeman, W. M., Jackson, J., Wren, J. D., Porter, H., & Richardson, A. (2019). The role of DNA methylation in epigenetics of aging. Pharmacology & Therapeutics, 195, 172–185. https://doi.org/10.1016/j.pharmthera.2018.11.001.
Unnikrishnan, A., Hadad, N., Masser, D. R., Jackson, J., Freeman, W. M., & Richardson, A. (2018). Revisiting the genomic hypomethylation hypothesis of aging. Annals of the New York Academy of Sciences, 1418(1), 69–79. https://doi.org/10.1111/nyas.13533.
Veiberg, V., Nilsen, E. B., Rolandsen, C. M., Heim, M., Andersen, R., Holmstrøm, F., Meisingset, E. L., & Solberg, E. J. (2020). The accuracy and precision of age determination by dental cementum annuli in four northern cervids. European Journal of Wildlife Research, 66(6), 91. https://doi.org/10.1007/s10344‐020‐01431‐9.
Wilkinson, G. S., Adams, D. M., Haghani, A., Lu, A. T., Zoller, J., Breeze, C. E., Arnold, B. D., Ball, H. C., Carter, G. G., Cooper, L. N., Dechmann, D. K. N., Devanna, P., Fasel, N. J., Galazyuk, A. V., Günther, L., Hurme, E., Jones, G., Knörnschild, M., Lattenkamp, E. Z., … Horvath, S. (2021). DNA methylation predicts age and provides insight into exceptional longevity of bats. Nature Communications, 12(1), 1615. https://doi.org/10.1038/s41467‐021‐21900‐2.
Zannas, A. S., Arloth, J., Carrillo‐Roa, T., Iurato, S., Röh, S., Ressler, K. J., Nemeroff, C. B., Smith, A. K., Bradley, B., Heim, C., Menke, A., Lange, J. F., Brückl, T., Ising, M., Wray, N. R., Erhardt, A., Binder, E. B., & Mehta, D. (2015). Lifetime stress accelerates epigenetic aging in an urban, African American cohort: Relevance of glucocorticoid signaling. Genome Biology, 16(1), 266. https://doi.org/10.1186/s13059‐015‐0828‐5.
معلومات مُعتمدة: 36905 Canadian Foundation for Innovation; RRG gme-665-ab ComputeCanada; RGPIN-2017-03934 Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
فهرسة مساهمة: Keywords: ageing; methylation; population genetics; wildlife management
تواريخ الأحداث: Date Created: 20240330 Date Completed: 20240603 Latest Revision: 20240603
رمز التحديث: 20240603
DOI: 10.1111/1755-0998.13956
PMID: 38553977
قاعدة البيانات: MEDLINE
الوصف
تدمد:1755-0998
DOI:10.1111/1755-0998.13956